Summer Sessions | Courses | Quantitative Methods: Social Sciences

Course information is posted for 2021. Please check back at a later time for updated 2022 course offerings.

Quantitative Methods: Social Sciences

Check the Directory of Classes for the most up-to-date course information.

Summer 2021 Session Information

  • SESSION A courses are May 3–June 18, 2021
  • SESSION B courses are June 28–August 16, 2021
Courses
Expand All
ORIENTATION
QMSS5000G001 0 points.

This course is a no-credit class designed to start providing critical material to incoming QMSS students overthe summer to help prepare them for the coding demands of the program. We will post links, exercises andresources for students to work on before they start their classes in the Fall of 2018.

Course Number Section/Call Number Session Times/Location
QMSS5000G001 001/11162 Full Trm Crs
Instructor Points Enrollment Method of Instruction
Gregory Eirich
0 Closed for Online Registration On-Line Only
ORIENTATION
QMSS5000G002 0 points.

This course is a no-credit class designed to start providing critical material to incoming QMSS students overthe summer to help prepare them for the coding demands of the program. We will post links, exercises andresources for students to work on before they start their classes in the Fall of 2018.

Course Number Section/Call Number Session Times/Location
QMSS5000G002 002/11163 Full Trm Crs
Instructor Points Enrollment Method of Instruction
Gregory Eirich
0 Closed for Online Registration On-Line Only
DATA ANALYSIS WITH PYTHON
QMSS5019S001 3 points.

This course is meant to provide an introduction to regression and applied statistics for the social sciences, with a strong emphasis on utilizing the Python software language to perform the key tasks in the data analysis workflow. Topics to be covered include various data structures, basic descriptive statistics, regression models, multiple regression analysis, interactions, polynomials, Gauss-Markov assumptions and asymptotics, heteroskedasticity and diagnostics, data visualization, models for binary outcomes, models for ordered data, first difference analysis, factor analysis, and cluster analysis. Through a variety of lab assignments, students will be able to generate and interpret quantitative data in helpful and provocative ways. Only relatively basic mathematics skills are assumed, but some more advanced math will be introduced as needed. A previous introductory statistics course that includes linear regression is helpful, but not required.

Course Number Section/Call Number Session Times/Location
QMSS5019S001 001/10772 Summer A Subterm Fr 10:10 AM–12:00 PM
Th 10:10 AM–12:00 PM

Instructor Points Enrollment Method of Instruction
Gregory Eirich
3 Closed for Online Registration Hybrid
NATURAL LANG PROCESSING SOCIAL SCIENCES
QMSS5067G001 3 points.

Social scientists need to engage with natural language processing (NLP) approaches that are found in computer science, engineering, AI, tech and in industry. This course will provide an overview of natural language processing as it is applied in a number of domains. The goal is to gain familiarity with a number of critical topics and techniques that use text as data, and then to see how those NLP techniques can be used to produce social science research and insights. This course will be hands-on, with several large-scale exercises. The course will start with an introduction to Python and associated key NLP packages and github. The course will then cover topics like language modeling; part of speech tagging; parsing; information extraction; tokenizing; topic modeling; machine translation; sentiment analysis; summarization; supervised machine learning; and hidden Markov models. Prerequisites are basic probability and statistics, basic linear algebra and calculus. The course will use Python, and so if students have programmed in at least one software language, that will make it easier to keep up with the course.

Course Number Section/Call Number Session Times/Location
QMSS5067G001 001/10771 Summer B Subterm Mo 04:10 PM–06:00 PM
We 04:10 PM–06:00 PM

Instructor Points Enrollment Method of Instruction
Patrick Houlihan
3 Open for Enrollment
(auto-fill waitlist)
Hybrid
MACHINE LEARNING SOC SCI
QMSS5073S001 3 points.
Course Number Section/Call Number Session Times/Location
QMSS5073S001 001/10770 Summer A Subterm Mo 04:10 PM–06:00 PM
We 04:10 PM–06:00 PM

Instructor Points Enrollment Method of Instruction
Michael Parrott
3 Closed for Online Registration Hybrid
INDEPENDENT STUDY
QMSS5997G001 4 points.

This course offers students an opportunity to expand their curriculum beyond the established course offerings. Interested parties must consult with the QMSS Program Director before adding the class. This course may be taken for 2-4 points.

Course Number Section/Call Number Session Times/Location
QMSS5997G001 001/10857 Summer A Subterm
Instructor Points Enrollment Method of Instruction
Gregory Eirich
4 Closed for Online Registration Hybrid
INDEPENDENT STUDY
QMSS5997G002 4 points.

This course offers students an opportunity to expand their curriculum beyond the established course offerings. Interested parties must consult with the QMSS Program Director before adding the class. This course may be taken for 2-4 points.

Course Number Section/Call Number Session Times/Location
QMSS5997G002 002/11172 Summer B Subterm
Instructor Points Enrollment Method of Instruction
Gregory Eirich
4 Open for Enrollment
(auto-fill waitlist)
Hybrid